A novel approach for deterioration and damage identification in building structures based on Stockwell-Transform and deep convolutional neural network Article Swipe
Vahidreza Gharehbaghi
,
Hashem Kalbkhani
,
Ehsan Noroozinejad Farsangi
,
T.Y. Yang
,
Andy Nguyễn
,
Seyedali Mirjalili
,
Christian Málaga‐Chuquitaype
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1080/24705314.2021.2018840
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1080/24705314.2021.2018840
In this paper, a novel deterioration and damage identification procedure (DIP) is presented and applied to building models. The challenge associated with applications on these types of structures is related to the strong correlation of responses, an issue that gets further complicated when coping with real ambient vibrations with high levels of noise. Thus, a DIP is designed utilizing low-cost ambient vibrations to analyze the acceleration responses using the Stockwell transform (ST) to generate spectrograms. Subsequently, the ST outputs become the input of two series of Convolutional Neural Networks (CNNs) established for identifying deterioration and damage on the building models. To the best of our knowledge, this is the first time that both damage and deterioration are evaluated on building models through a combination of ST and CNN with high accuracy.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/24705314.2021.2018840
- OA Status
- green
- Cited By
- 13
- References
- 33
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3211689486Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/24705314.2021.2018840Digital Object Identifier
- Title
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A novel approach for deterioration and damage identification in building structures based on Stockwell-Transform and deep convolutional neural networkWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2022Year of publication
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2022-03-13Full publication date if available
- Authors
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Vahidreza Gharehbaghi, Hashem Kalbkhani, Ehsan Noroozinejad Farsangi, T.Y. Yang, Andy Nguyễn, Seyedali Mirjalili, Christian Málaga‐ChuquitaypeList of authors in order
- Landing page
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https://doi.org/10.1080/24705314.2021.2018840Publisher landing page
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://research-repository.griffith.edu.au/bitstreams/dcc548e2-dea0-4c04-9564-7959b55f8aae/downloadDirect OA link when available
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Convolutional neural network, Identification (biology), Artificial intelligence, Computer science, Pattern recognition (psychology), Artificial neural network, Deep learning, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
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13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 5, 2023: 3, 2022: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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